UID: REQ-002
Status: Active
Relations:

• File, file.py, hello_world
• File, file.rs, <file::FooTuple as file::FooTrait>::bar

Statement: The system shall show "Hello".

• Visit fmhy.net/android-iosguide#ios-ipas for more sources.
• Sideloading Guide: https://rentry.co/sideloadingguide, https://ios.cfw.guide/sideloading-apps/

If you want to setup the official GeforceNow app on Linux for gaming, use:

• flatpak install -y --user flathub org.freedesktop.Sdk//24.08 org.freedesktop.Platform//24.08
• flatpak remote-add --user --if-not-exists GeForceNOW https://international.download.nvidia.com/GFNLinux/flatpak/geforcenow.flatpakrepo
• flatpak install -y --user GeForceNOW com.nvidia.geforcenow
• flatpak run com.nvidia.geforcenow
• If you are on Wayland and the Window doesn't open: flatpak override --user --nosocket=wayland com.nvidia.geforcenow, then launch again.

Alternatively:

I'm using a small "Pico" or "Zero" dev board purchased from Amazon. It has a USB-C connector and numerous GPI pins broken out (0-15, 26-29).

Plug this board to the klipper host computer and use lsusb and you'll find this entry:

Bus 001 Device 005: ID 2e8a:0003 Raspberry Pi RP2 Boot

Seeing this will tell you that your computer is able to establish a usb connection to the dev board and you can now proceed with building and flashing Klipper.

Markdown-only workbook for Exam GH-600: Developing in Agentic AI Systems.

Last updated: May 24, 2026.

This version is organized around the official GH-600 domains. Each domain explains the concepts, shows the GitHub implementation artifacts, and includes examples you should be able to read in YAML, Markdown, CLI output, PR timelines, and audit logs.

Public sharing note: this guide is not an exam dump and does not contain real exam questions or answer choices. It is a structured study workbook built from official Microsoft and GitHub documentation, with practical examples written for learning and review.

This will install node js and allow you to use claude code in a docker container on unraid use the compose manager plugin.

Use at your own risk This will give AI access to whatever files you pass to the container (if using filesystem mcp).

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.